The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.
A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery
Open accessAbstract
In the coming years, artificial intelligence (AI) will pervade almost every aspect of the health care delivery system. AI has the potential to improve patient safety (e.g., diagnostic accuracy) as well as reduce the burden on clinicians (e.g., documentation-related workload); however, these benefits are yet to be realized. AI is only one element of a larger sociotechnical system that needs to be considered for effective AI application. In this paper, we describe the current challenges of integrating AI into clinical care and propose a sociotechnical systems (STS) approach for AI design and implementation. We demonstrate the importance of an STS approach through a case study on the design and implementation of a clinical decision support (CDS). In order for AI to reach its potential, the entire work system as well as clinical workflow must be systematically considered throughout the design of AI technology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“At the sociotechnical level, the framework treats the clinician-AI diagnostic interaction as a system in which computational tools, institutional workflows, regulatory requirements, and human reasoning are mutually constitutive”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Salwei and Carayon, 2022
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The integration of artificial intelligence (AI) in health care settings demands a nuanced approach that considers both technical performance and sociotechnical factors.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
The cited source says what the claim attributes to it.
In the citing paper
“Their model demonstrates that an AI can only integrate into clinical workflows if it fits within the context, or the work system, in which it is implemented.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
The cited source says what the claim attributes to it.
In the citing paper
“Salwei and Carayon [1] developed a sociotechnical systems framework for AI that acknowledges the social and technical aspects of work that relate to the successful design and implementation of AI.”
From Checklist Approach to Developing and Implementing AI in Clinical Settings: Instrument Development Study (2025) · cites this paper as [1]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Trist, E. L., & Bamforth, K. W. (1951) · Human Relations · doi:10.1177/001872675100400101 · Cited by 3,379
Work system design for patient safety: the SEIPS model
Carayon, P., Schoofs Hundt, A., Karsh, B.-T., Gurses, A. P., Alvarado, C. J., Smith, M., & Flatley Brennan, P. (2006) · Quality in Health Care · doi:10.1136/qshc.2005.015842 · Cited by 1,795 · cited 1×
“Early in the development of AI technologies, designers need to consider how feedback loops will be integrated in the work system to support the monitoring of the technology in real use and to help identify problems as they arise (Carayon et al., 2006, 2014).”
BackgroundSelf-citationHuman-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
Shneiderman, B. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1741118 · Cited by 1,743
Wells, P. S., Anderson, D. R., Rodger, M. A., Stiell, I. G., Dreyer, J. F., Barnes, D., Forgie, M., Kovacs, G. J., Ward, J., & Kovacs, M. J. (2001) · Annals of Internal Medicine · doi:10.7326/0003-4819-135-2-200107170-00010 · Cited by 1,431
Artificial intelligence, bias and clinical safety
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956
Clinical decision support in the era of artificial intelligence
Shortliffe, E. H., & Sepúlveda, M. J. (2018) · JAMA · doi:10.1001/jama.2018.17163 · Cited by 920
From Here to Autonomy: Lessons Learned From Human–Automation Research
Endsley, M. R. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816681350 · Cited by 898
Human factors systems approach to healthcare quality and patient safety
Carayon, P., Wetterneck, T. B., Rivera-Rodriguez, A. J., Hundt, A. S., Hoonakker, P., Holden, R., & Gürses, A. P. (2013) · Applied Ergonomics · doi:10.1016/j.apergo.2013.04.023 · Cited by 814
Self-citationHuman factors of complex sociotechnical systems
Carayon, P. (2006) · Applied Ergonomics · doi:10.1016/j.apergo.2006.04.011 · Cited by 668
Self-citationBeede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376718 · Cited by 614
A balance theory of job design for stress reduction
Smith, M. J., & Sainfort, P. C. (1989) · International Journal of Industrial Ergonomics · doi:10.1016/0169-8141(89)90051-6 · Cited by 488
Fundamentals of systems ergonomics/human factors
Wilson, J. R. (2013) · Applied Ergonomics · doi:10.1016/j.apergo.2013.03.021 · Cited by 483
Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0015-z · Cited by 443
Innovating health care: key characteristics of human-centered design
Melles, M., Albayrak, A., & Goossens, R. (2020) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzaa127 · Cited by 421
Beyond usability: Designing effective technology implementation systems to promote patient safety
Karsh, B.-T. (2004) · Quality and Safety in Health Care · doi:10.1136/qshc.2004.010322 · Cited by 414
Raja, A. S., Greenberg, J. O., Qaseem, A., Denberg, T. D., Fitterman, N., & Schuur, J. D. (2015) · Annals of Internal Medicine · doi:10.7326/m14-1772 · Cited by 341
Clinical Decision Rules for Excluding Pulmonary Embolism: A Meta-analysis
Lucassen, W. A. M., Geersing, G., Erkens, P. M. G., Reitsma, J. B., Moons, K. G. M., Buller, H. R., & van Weert, H. C. P. M. (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-7-201110040-00007 · Cited by 324
Presenting machine learning model information to clinical end users with model facts labels
Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0253-3 · Cited by 279
Quality and safety of healthcare
Donaldson, L., & Rutter, P. D. (2017) · doi:10.1201/9781315154657-7 · Cited by 255
Human–machine partnership with artificial intelligence for chest radiograph diagnosis
Patel, B. N., Rosenberg, L., Willcox, G., Baltaxe, D., Lyons, M., Irvin, J., Rajpurkar, P., Amrhein, T., Gupta, R., Halabi, S., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0189-7 · Cited by 254
Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0099-8 · Cited by 233
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril
Series, T. L. H. S., & National Academy of Medicine (2019) · doi:10.17226/27111 · Cited by 227
Handbook of human factors and ergonomics in health care and patient safety
Finnerty, J. (2012) · Ergonomics · doi:10.1080/00140139.2012.740845 · Cited by 208
The Balance Theory and the Work System Model … Twenty Years Later
Carayon, P. (2009) · International Journal of Human-Computer Interaction · doi:10.1080/10447310902864928 · Cited by 195
Self-citationThe DECIDE-AI Steering Group (2021) · Nature Medicine · doi:10.1038/s41591-021-01229-5 · Cited by 184
Großmann, C., Powers, B. W., & McGinnis, J. M. (2011) · Cited by 163
Meyer, A. N. D., Giardina, T. D., Spitzmueller, C., Shahid, U., Scott, T. M. T., & Singh, H. (2019) · Journal of Medical Internet Research · doi:10.2196/14679 · Cited by 162
Collaborative design: Managing task interdependencies and multiple perspectives
Détienne, F. (2005) · Interacting with Computers · doi:10.1016/j.intcom.2005.05.001 · Cited by 159
Human Factors Challenges for the Safe Use of Artificial Intelligence in Patient Care
Sujan, M., Furniss, D., Grundy, K., Grundy, H., Nelson, D., Elliott, M., White, S., Habli, I., & Reynolds, N. (2019) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100081 · Cited by 144
Smith, M. J., & Carayon, P. (1995) · International Journal of Human Factors in Manufacturing · doi:10.1002/hfm.4530050107 · Cited by 138
Self-citationGoverning the safety of artificial intelligence in healthcare
Macrae, C. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2019-009484 · Cited by 123
The Last Mile: Where Artificial Intelligence Meets Reality
Coiera, E. (2019) · Journal of Medical Internet Research · doi:10.2196/16323 · Cited by 102
Okolo, C. T., Kamath, S., Dell, N. L., & Vashistha, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445420 · Cited by 100
Humanizing Artificial Intelligence
Israni, S. T., & Verghese, A. (2018) · JAMA · doi:10.1001/jama.2018.19398 · Cited by 94
Pangti, R., Mathur, J., Chouhan, V., Kumar, S. M., Rajput, L., Shah, S., Gupta, A., Dixit, A., Dholakia, D., Gupta, S., et al. (2020) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.16967 · Cited by 87
Trust in AI: why we should be designing for APPROPRIATE reliance
Benda, N. C., Novak, L. L., Reale, C., & Ancker, J. S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab238 · Cited by 76
Salwei, M. E., Carayon, P., Hoonakker, P., Hundt, A. S., Wiegmann, D. A., Pulia, M. S., & Patterson, B. W. (2021) · Applied Ergonomics · doi:10.1016/j.apergo.2021.103498 · Cited by 73
Self-citationPetersen, C. C., Smith, J. R. L., Freimuth, R. R., Goodman, K. W., Jackson, G. P., Kannry, J. L., Liu, H., Madhavan, S., Sittig, D. F., & Wright, A. T. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa319 · Cited by 66
Carayon, P., Hoonakker, P., Hundt, A. S., Salwei, M. E., Wiegmann, D. A., Brown, R., Kleinschmidt, P. C., Novak, C., Pulia, M. S., Wang, Y., et al. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2019-009857 · Cited by 64
Self-citationMachine learning in clinical practice: prospects and pitfalls
Scott, I. A., Cook, D., Coiera, E. W., & Richards, B. (2019) · The Medical Journal of Australia · doi:10.5694/mja2.50294 · Cited by 54
Artificial intelligence in medical devices and clinical decision support systems
Sloane, E. B., & Silva, R. J. (2019) · Clinical Engineering Handbook · doi:10.1016/b978-0-12-813467-2.00084-5 · Cited by 50
An awakening in medicine: the partnership of humanity and intelligent machines
Celi, L. A., Fine, B., & Stone, D. J. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30127-x · Cited by 45
Lesselroth, B. J., Yang, J., McConnachie, J., Brenk, T., & Winterbottom, L. (2011) · BMJ Quality & Safety · doi:10.1136/bmjqs.2010.042689 · Cited by 37
Carayon, P., & Salwei, M. E. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab002 · Cited by 36
Self-citationKhan, S., Richardson, S. I., Liu, A. C., Mechery, V., McCullagh, L., Schachter, A., Pardo, S., & McGinn, T. G. (2018) · JMIR Human Factors · doi:10.2196/10245 · Cited by 29
Kline, J. A., Peterson, C. E., & Steuerwald, M. (2010) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.2010.00826.x · Cited by 27
HUMAN FACTORS AND ERGONOMICS IN HEALTH CARE
Carayon, P., Wust, K. L., Hose, B., & Salwei, M. E. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch53 · Cited by 24
Self-citationThe Design of PE Dx, a CDS to Support Pulmonary Embolism Diagnosis in the ED
Hoonakker, P., Carayon, P., Salwei, M. E., Hundt, A. S., Wiegmann, D. A., Kleinschmidt, P. C., Pulia, M. S., Wang, Y., Novak, C., & Patterson, B. W. (2019) · Studies in health technology and informatics · doi:10.3233/shti190152 · Cited by 12
Self-citationCarayon, P., Wetterneck, T. B., Hundt, A. S., Rough, S. S., & Schroeder, M. E. (2008) · Contributions to management science · doi:10.1007/978-3-7908-2046-1_9 · Cited by 11
Self-citationGarvey, K. V., Craig, K. J. T., Russell, R. G., Novak, L. L., Moore, D. E., Preininger, A. M., Jackson, G. P., & Miller, B. M. (2021) · Medical Science Educator · doi:10.1007/s40670-021-01377-w · Cited by 11
Warden, T., Carayon, P., Roth, E. M., Chen, J. Z.-J., Clancey, W. J., Hoffman, R. R., & Steinberg, M. L. (2019) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181319631100 · Cited by 10
Self-citationHuman Factors in Health(care) Informatics: Toward Continuous Sociotechnical System Design
Carayon, P. (2019) · Studies in health technology and informatics · doi:10.3233/shti190131 · Cited by 9
Self-citationHow Artificial Intelligence Is Changing Health Care Delivery
Sanders, S. F., Terwiesch, M., Gordon, W. J., & Stern, A. D. (2019) · Digital Access to Scholarship at Harvard (DASH) (Harvard University) · Cited by 7
(2020) · Inside Precision Medicine · doi:10.1089/clinomi.07.s1.26 · Cited by 4
Furniss, D. J., Habli, I., Sujan, M. A., & Hawkins, R. (2020) · Safety-Critical Systems Symposium (SSS) · doi:10.65391/r1186 · Cited by 4
Research works printed but not linked: 11
- Carayon P, 2019, Studies in Health Technology and Informatics, 265, 22
- Hoonakker P. L. T., 2019, Studies in Health Technology and Informatics, 265, 134
- International Organization for Standardization, 2010, ISO 9241-210 ergonomics of human-system interaction - Part 210: Human-centred design for interactive systems
- Israni S. T., 2019, Jsls: Journal of the Society of Laparoendoscopic Surgeons, 321, 29
- Lesselroth B. J., 2011, Quality and safety in health care, 20, 042689
- Salwei M. E., 2021, Applied Ergonomics, 97, 1
- Shortliffe E. H., 2018, Jsls: Journal of the Society of Laparoendoscopic Surgeons, 320, 2199
- Sujan M., 2021, Human factors and ergonomics in healthcare
- Trist E., 1981, Perspectives on Organizational Design and Behavior, 1
- Vasey B., 2021, Nature Medicine, 27, 186
- Zimlichman E., 2021, The coming AI transformation of care delivery
Cited by
Sorted by citations
Wenderott, K., Krups, J., Zaruchas, F., & Weigl, M. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01248-9 · Cited by 161
Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework
van der Vegt, A. H., Scott, I. A., Dermawan, K., Schnetler, R. J., Kalke, V. R., & Lane, P. J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad088 · Cited by 109
Rony, M. K. K., Parvin, M. R., Wahiduzzaman, M., Debnath, M., Bala, S. D., & Kayesh, I. (2024) · SAGE Open Nursing · doi:10.1177/23779608241245220 · Cited by 117
Achieving large-scale clinician adoption of AI-enabled decision support
Scott, I. A., van der Vegt, A., Lane, P., McPhail, S., & Magrabi, F. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100971 · Cited by 65
Finkelstein, J., Gabriel, A. S., Schmer, S., Truong, T., & Dunn, A. S. (2024) · Journal of Medical Systems · doi:10.1007/s10916-024-02104-9 · Cited by 76
Metadata
- Authors
- Salwei, M. E., & Carayon, P.
- Affiliations
- Vanderbilt University Medical Center (US)
- University of Wisconsin–Madison (US)
- Corresponding author
- Salwei, Megan E.
- Venue
- Journal of Cognitive Engineering and Decision Making
- Publisher
- SAGE Publications
- Published
- 2022-05-11
- Volume
- 16
- Issue
- 4
- Pages
- 194-206
- ISSN
- 1555-3434 (ISSN-L)
- PMC
- PMC9873227
- OpenAlex
- W4280579451
- Cited by
- 112
- References
- 56
- Citations by year
- 2026: 58 · 2025: 29 · 2024: 18 · 2023: 5 · 2022: 2
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Cognitive Engineering and Decision Making (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Center for Advancing Translational Sciences (1UL1TR002373)
- Agency for Healthcare Research and Quality (K12HS026395, R01HS022086)
- U.S. National Library of Medicine (T15LM007450-19)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Keywords
- artificial intelligence in healthcare, socio-technical systems, clinical decision support, clinical workflow, patient safety, clinician workload, work system design, AI implementation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
